February 2007

Adventures in Identification II: Exposing Corrupt Politicians

Today we continue our voyage in the treasure quest for identification in observational studies. After our sojourn in Spain two weeks ago, the next stopover is in Brazil, where in a recent paper Claudio Ferraz and Frederico Finan discovered a nice natural experiment that allows to estimate the effect of transparency on political accountability. Many in the policy world are agog over the beneficial impact of...

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Applied Statistics - Donald Rubin

This week, the Applied Statistics Workshop will present a talk by Donald Rubin, the John Loeb Professor of Statistics at Harvard. Professor Rubin has published widely on numerous topics in statistics, and is perhaps best known for his work on missing data and causal inference. His articles have appeared in over thirty journals, and he is the author or co-author of several books on missing data, causal inference, and Bayesian data analysis, many of which are the standards in their fields. In 1995, Professor Rubin received the Samuel S. Wilks Memorial Award from the American Statistical...

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Translating Statistics-Speak

I wish we all talked more about how scientific results are translated by the media. Fully understanding the assumptions and limitations of a study is challenging enough for those performing the research. In some ways, the journalists’ job is harder, finding lay language to summarize outcomes and implications without generalizing or ignoring uncertainty. I do not envy them the task.

Byron Calame, the public editor of the New York Times, recently discussed...

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Cheating for Honest People

Let me follow up on yesterday’s post by Jim Greiner.

Jim’s problem: He’s touring the country touting tools for increased honesty in applied statistical research, only to be asked, effectively, for recommendations about using these tools to cheat more effectively. Yay academic job market.

Jim’s example goes like this: An analyst is asked to model the effect of a treatment, T, on the outcome, Y, while controlling for a bunch of confounders, X. To minimize the potential for data dredging we give the analyst only the treatment and the observed potential confounders to model the...

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How do I cheat with potential outcomes?

As some folks know, I'm on the legal academic job market this year. My job talk paper is on the application of the potential outcomes framework for causation to legal matters, particularly anti-discrimination issues that arise in litigation. As I've presented the framework, I've highlighted one of its advantages as being the fact that much of the hard work of separating covariates from intermediate outcomes and balancing covariates can (and should) be done without access to the outcome variable. The idea is that without access to the outcome variable, it is harder for a researcher (or,...

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Applied Statistics - Dan Hopkins

This week, the Applied Statistics Workshop will present a talk by Dan Hopkins, a Ph.D. candidate at in the Government Department at Harvard. Dan has a long-standing association with Harvard, having graduated from the College in 2000. His research focuses on political behavior, state and local politics, and political methodology. His work has appeared in the American Political Science Review. He will present a talk entitled "Flooded Communities: Estimating the Post-Katrina Migration's Impact on Attitudes towards the Poor and...

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Initiative for Innovative Computing - Edward Tufte

The Initiative in Innovative Computing, an interdisciplinary program that aims to "foster the creative use of computational resources to address issues at the forefront of data-intensive science," is hosting a talk by Edward Tufte next week. It is easy to forget that Tufte began his career as a political scientist, long before he became known for his work on the visual representation of evidence. His 1975 article on "Determinants of the Outcomes of Midterm Elections" is one of the 20 most-cited articles...

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Data sharing and visualization

A friend of mine pointed me to this website, Many eyes. Basically any random person can upload any sort of dataset, visualize the dataset in any number of ways, and then make the results publically available so that anyone can see them.

The negative, of course, is much the same as with anything that "just anyone" can contribute to: there is a lot of useless stuff, and (if the source of the dataset is uncited) you don't know for sure how valid the dataset itself is. There may be a lot of positives, though: the volume...

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Adventures in Identification I: Voting After the Bomb

Jens Hainmueller

I've decided to start a little series of entries under the header `Adventures in Identification.' The title is inspired by the increasing trend in the social sciences, in particular economics, public health, also political science, sociology, etc. to look for natural or quasi-experiments to identify causal effects in observational settings. Although there are of course plenty of bad examples of this type of study, I think the general line of research is very promising and the...

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